changes website

app.py

@@ -4,11 +4,17 @@ import os
 import pandas as pd
 import tomli
 
+
 pd.options.mode.chained_assignment = None
 
+
 st.set_page_config(layout="wide")
 
-# Test change
+
+st.markdown(
+    "<div style='background-color: lightblue; text-align: center; padding: 10px;'><h1 style='font-size: 70px;'>Our History in Data</h1></div>",
+    unsafe_allow_html=True,
+)
 
 
 @st.cache_data
@@ -41,135 +47,176 @@ def get_region_description(region_data, selected_region):
 region_data = load_region_descriptions()
 
 
-st.sidebar.title("Our History in Data")
-st.sidebar.write(
-    "This project is led by Charles de Dampierre, Folgert Karsdorp, Mike Kestemont, Valentin Thouzeau and Nicolas Baumard"
+# page = st.sidebar.selectbox("Navigate to:", ["Home", "Methodology", "Team"])
+page = st.sidebar.radio(
+    "Menu",
+    ["Home", "Methodology", "Team", "About"],
+    key="navigation_radio",
 )
 
-# Set the global index path
-global_index_path = "data/immaterial_index/figures_trends_R/results"
-global_index_path_per_capita = (
-    "data/immaterial_index/figures_trends_R/results_per_capita"
-)
-unseen_index_path = (
-    "data/immaterial_index/figures_trends_R/figures_unseen/results_unseen"
-)
-unseen_capita_index_path = (
-    "data/immaterial_index/figures_trends_R/figures_unseen/results_unseen/per_capita"
+
+st.sidebar.success(
+    "This project is led by Charles de Dampierre, Folgert Karsdorp, Mike Kestemont, Valentin Thouzeau and Nicolas Baumard"
 )
 
 
-population_path = "data/population"
-maps_path = "data/map_figures"
-
-
-from region_list import region_list
-
-region_filtered = list(region_list.keys())
-
-index_paths = {}
-
-for region_key in region_list:
-    # Create the index paths for the current region
-    index_paths[region_key] = {
-        "map": f"{maps_path}/map_{region_key}.png",
-        "global_index": f"{global_index_path}/{region_key}.png",
-        "global_index_per_capita": f"{global_index_path_per_capita}/{region_key}.png",
-        "unseen_index": f"{unseen_index_path}/{region_key}.png",
-        "unseen_index_capita": f"{unseen_capita_index_path}/{region_key}.png",
-        "population_index": f"{population_path}/{region_key}.png",
-    }
-
-# Get the region names (keys) from the index_paths dictionary
-regions = list(index_paths.keys())
-
-# Allow the user to select a region
-selected_region = st.sidebar.selectbox("Region:", regions, index=regions.index("Japan"))
-
-# Display the selected region's images vertically
-if selected_region in index_paths:
-    col1, col2 = st.columns(2)
-
-    df = df_ind[df_ind["region_name"] == selected_region]
-    df = df.drop(["region_name", "decade"], axis=1)
-    df = df[
-        [
-            "individual_name",
-            "productive_year",
-            "score",
-            "individual_wikidata_id" "",
-        ]
-    ]
-    df = df.sort_values("score", ascending=False)
-    df = df.rename(columns={"score": "Number of Catalogs"})
-
-    min_date = region_list[selected_region]["time_range"][0]
-    max_date = region_list[selected_region]["time_range"][1]
-    df = df[df["productive_year"] >= min_date]
-    df = df[df["productive_year"] <= max_date]
-    df["productive_year"] = df["productive_year"].astype(int)
-    df = df.reset_index(drop=True)
-
-    # Display the data in the left column
-    with col1:
-        st.header("Cultural Producers")
-        st.dataframe(df)
-        st.write(f"Number of Cultural producers active before 1800: {len(df)}")
-
-        for key, path in index_paths[selected_region].items():
-            if os.path.exists(path):
-
-                if key == "global_index":
-                    st.subheader("Global Index")
-                    st.image(
-                        Image.open(path),
-                        caption=key.capitalize(),
-                        use_column_width=True,
-                    )
-                elif key == "global_index_per_capita":
-                    st.subheader("Index per capita")
-                    st.image(
-                        Image.open(path),
-                        caption=key.capitalize(),
-                        use_column_width=True,
-                    )
-                elif key == "unseen_index":
-                    st.subheader("Unsee-Species Index")
+# Test change
+if page == "Home":
+
+    # Set the global index path
+    global_index_path = "data/immaterial_index/figures_trends_R/results"
+    global_index_path_per_capita = (
+        "data/immaterial_index/figures_trends_R/results_per_capita"
+    )
+    unseen_index_path = (
+        "data/immaterial_index/figures_trends_R/figures_unseen/results_unseen"
+    )
+    unseen_capita_index_path = "data/immaterial_index/figures_trends_R/figures_unseen/results_unseen/per_capita"
+
+    population_path = "data/population"
+    maps_path = "data/map_figures"
+
+    from region_list import region_list
+
+    region_filtered = list(region_list.keys())
+
+    index_paths = {}
+
+    for region_key in region_list:
+        # Create the index paths for the current region
+        index_paths[region_key] = {
+            "map": f"{maps_path}/map_{region_key}.png",
+            "global_index": f"{global_index_path}/{region_key}.png",
+            "global_index_per_capita": f"{global_index_path_per_capita}/{region_key}.png",
+            "unseen_index": f"{unseen_index_path}/{region_key}.png",
+            "unseen_index_capita": f"{unseen_capita_index_path}/{region_key}.png",
+            "population_index": f"{population_path}/{region_key}.png",
+        }
+
+    # Get the region names (keys) from the index_paths dictionary
+    regions = list(index_paths.keys())
+
+    # Allow the user to select a region
+    selected_region = st.sidebar.selectbox(
+        "Region:", regions, index=regions.index("Japan")
+    )
+
+    # Display the selected region's images vertically
+    if selected_region in index_paths:
+        st.markdown(
+            f"<h1 style='text-align: left; font-size: 50px;'>{selected_region}</h1>",
+            unsafe_allow_html=True,
+        )
+
+        try:
+            st.image(
+                f"image/{selected_region}.jpeg",
+                caption="Japan",
+                use_column_width=False,
+                width=1000,
+            )
+        except:
+            pass
+
+        col1, col2, col3 = st.columns([8, 1, 8])
+
+        # Display the data in the left column
+        with col1:
+            for key, path in index_paths[selected_region].items():
+                if os.path.exists(path):
+
+                    if key == "global_index":
+                        st.subheader("Cultural Index")
+                        st.image(
+                            Image.open(path),
+                            caption=key.capitalize(),
+                            use_column_width=True,
+                        )
+                    elif key == "global_index_per_capita":
+                        st.subheader("Cultural Index per capita")
+                        st.image(
+                            Image.open(path),
+                            caption=key.capitalize(),
+                            use_column_width=True,
+                        )
+                    elif key == "unseen_index":
+                        st.subheader(
+                            "Cultural Index corrected by the unseen-species model"
+                        )
+                        print(path)
+                        st.image(
+                            Image.open(path),
+                            caption=key.capitalize(),
+                            use_column_width=True,
+                        )
+                    elif key == "unseen_index_capita":
+                        st.subheader(
+                            "Cultural Index per capita corrected by the unseen-species model"
+                        )
+                        st.image(
+                            Image.open(path),
+                            caption=key.capitalize(),
+                            use_column_width=True,
+                        )
+
+                else:
+                    st.write(f"File for {key.capitalize()} does not exist.")
+
+            with col3:
+
+                try:
                     st.image(
-                        Image.open(path),
-                        caption=key.capitalize(),
+                        Image.open(f"data/map_figures/map_{selected_region}.png"),
                         use_column_width=True,
+                        width=1000,
                     )
-                elif key == "unseen_index_capita":
-                    st.subheader("Unsee-Species per capita Index")
-                    st.image(
-                        Image.open(path),
-                        caption=key.capitalize(),
-                        use_column_width=True,
+                except:
+                    pass
+
+                st.subheader("Analysis")
+                try:
+                    region_description = get_region_description(
+                        region_data, selected_region
                     )
-                elif key == "population_index":
-                    st.subheader("Population Index")
+                    st.write(f"{region_description}")
+                except:
+                    st.write("Analysis not ready yet")
+
+                st.subheader("Cultural Producers in Wikidata")
+                df = df_ind[df_ind["region_name"] == selected_region]
+                df = df.drop(["region_name", "decade"], axis=1)
+                df = df[
+                    [
+                        "individual_name",
+                        "productive_year",
+                        "score",
+                        "individual_wikidata_id" "",
+                    ]
+                ]
+                df = df.sort_values("score", ascending=False)
+                df = df.rename(columns={"score": "Number of Catalogs"})
+
+                min_date = region_list[selected_region]["time_range"][0]
+                max_date = region_list[selected_region]["time_range"][1]
+                df = df[df["productive_year"] >= min_date]
+                df = df[df["productive_year"] <= max_date]
+                df["productive_year"] = df["productive_year"].astype(int)
+                df = df.reset_index(drop=True)
+                st.dataframe(df)
+                st.write(f"Number of Cultural producers active before 1800: {len(df)}")
+
+                try:
+                    st.subheader("Population")
                     st.image(
                         Image.open(path),
                         caption=key.capitalize(),
                         use_column_width=True,
                     )
-                elif key == "map":
-                    st.subheader("Maps")
-                    st.sidebar.image(
-                        Image.open(path),
-                        caption=key.capitalize(),
-                        use_column_width=True,
-                    )
-            else:
-                st.write(f"File for {key.capitalize()} does not exist.")
-
-        with col2:
-            try:
-                region_description = get_region_description(
-                    region_data, selected_region
-                )
-                st.header("Analysis")
-                st.write(f"{region_description}")
-            except:
-                st.write("Analysis not ready yet")
+                except:
+                    pass
+
+
+elif page == "Methodology":
+    # Add content for the Methodology section here
+    st.markdown("<h2>Methodology</h2>", unsafe_allow_html=True)
+    st.write("Here you can describe the methodology used in your project.")

regions.toml

@@ -17,6 +17,7 @@ century and recovery from the 13th to 16th centuries Nakabayashi et al. (2020).
 
 In line with this study Nakabayashi et al. (2020), our results also suggest that the recovery of the economic development began in
 the 13th during the Kamakura shogunate, earlier than what was previously estimated.
+
 Our result also allows to have finer temporal estimation. For instance, we observe that the isolation of
 the country (the sakoku policy) starting in 1633 did not impact economic cultural production. At the other
 end of the period, we can see that cultural production was already very robust before the Meiji reforms